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This article introduces the concern that exists in the wider economic world concerning the developments carried out in Smart Cities. The various studies that have been developed capture the economic approach by focusing on specific economic development theories. This article initially provides a theoretical response to the need for a joint approach to the different economic theories relating to Smart Cities, placing the bases of their development in the circular economy. Subsequently, the paper presents a device-based proposal to validate the sustainability principles indicated in the Smart Economy, focusing exclusively on the areas of health and mobility. As a whole, the work concludes with the need to incorporate sustainability criteria into economic ambition so that technological developments have a place in future Smart Cities.

In this research, a vagus nerve stimulator has been developed and miniaturized for use in epilepsy research. The board contains all the components necessary for its operation during the standard duration of the experiments, being possible to control it once implanted and even being able to reuse it. The VNS system has been designed for rodents since the VNS devices available for human are not only too large for laboratory animals, but also too expensive. With this solution the expenditure on materials made by laboratories is greatly reduced and bioethical considerations were kept in mind. The system was validated in hamsters. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy.

Doll and robot are effective and beneficial non-pharmacological therapies applied in different clinical settings. Doll therapy (DT), principally based in Bowlby's attachment theory, uses an empathy or lifelike baby doll to conduct caring behaviors of it. Robot therapies (RT) use care robots with a friendly attitude and appearance that create emotions and movements that lead to different verbal, motor and emotional reactions. Both DT and RT are person-centred therapies that aim to improve wellbeing of people that suffer from different neurological, psychological and mental health disorders, such as Alzheimer's Disease, autism spectrum disorder, stress or depression, by providing a realistic experience. In this paper, the characteristics of both therapies, their benefits and the possibilities of innovation in the therapeutic field are presented.

[ES] El taller sobre tecnologías de información y comunicación disruptivas para la innovación y la transformación digital, organizado bajo el alcance del proyecto disruptiva, tiene como objetivo discutir los problemas, desafíos y beneficios del uso de tecnologías digitales disruptivas, a saber, Internet de las cosas, Big data, computación en la nube, sistemas multi-agentes, aprendizaje automático, realidad virtual y aumentada, y robótica colaborativa, para apoyar la transformación digital en curso en la sociedad Temas Intelligent Manufacturing Systems Industry 4.0 and digital transformation Internet of Things Cyber-security Collaborative and intelligent robotics Multi-Agent Systems Industrial Cyber-Physical Systems Virtualization and digital twins Predictive maintenance Virtual and augmented reality Big Data and advanced data analytics Edge and cloud computing, [EN] The workshop on Disruptive Information and Communication Technologies for Innovation and Digital transformation, organized under the scope of the Disruptive project, aims to discuss problems, challenges and benefits of using disruptive digital technologies, namely Internet of Things, Big data, cloud computing, multi-agent systems, machine learning, virtual and augmented reality, and collaborative robotics, to support the on-going digital transformation in society Topics Intelligent Manufacturing Systems Industry 4.0 and digital transformation Internet of Things Cyber-security Collaborative and intelligent robotics Multi-Agent Systems Industrial Cyber-Physical Systems Virtualization and digital twins Predictive maintenance Virtual and augmented reality Big Data and advanced data analytics Edge and cloud computing

Spain’s agricultural and livestock sector is competitive and powerful on a global scale, especially in relation to fruit and vegetables, pork, wine and olive oil, which are produced and exported in large amounts. The development of the sector and of new technological advances makes it possible to generate innovative solutions adapted to the current needs of the market. Moreover, this helps improve the competitiveness of the producing companies. This paper uses the Internet of Things and Smart Agriculture to develop a weather station prepared for the current and future needs demanded by the sector. Thanks to its ability to transmit information using long-range and low-power consumption (LPWA) protocols such as LoRaWAN and Sigfox, the device can be deployed in remote areas several kilometres away from the nearest access point. One of the most significant values is creating a device capable of drastically reducing energy consumption and the requests for maintenance and replacement of the equipment and working with the most modern data transmission protocols suitable for the Internet of Things and industry 4.0. Likewise, compatibility with 5G networks has been a fundamental requirement to adapt the device to future needs.

It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time, workers are as safe and protected as possible. Thanks to Smart Personal Protective Equipment (PPE) and wearable technologies, information about the workers and their environment can be extracted to reduce the rate of accidents and occupational illness, leading to a significant improvement. This article proposes an architecture that employs three pieces of PPE: a helmet, a bracelet and a belt, which process the collected information using artificial intelligence (AI) techniques through edge computing. The proposed system guarantees the workers’ safety and integrity through the early prediction and notification of anomalies detected in their environment. Models such as convolutional neural networks, long short-term memory, Gaussian Models were joined by interpreting the information with a graph, where different heuristics were used to weight the outputs as a whole, where finally a support vector machine weighted the votes of the models with an area under the curve of 0.81.